Delivering Value from Data Analytics

Even as high drug prices have grabbed headlines over the last year, it doesn’t paint a clear or complete picture of what’s happening in the pharmaceutical industry. Pharmaceutical companies are significant drivers of innovation, and provide new and cutting-edge therapies for patients around the world. Consider that although total pharmaceutical industry revenues topped $1 trillion as of 2014 – and are projected to reach $1.2 trillion by 2022 – actual annual growth rates have remained dismally stagnant at just over four percent[1] for several years.

It’s not for lack of investment. Pharmaceutical companies have made enormous investments in research and development and innovation, and more specifically in data, analytics, and digital health. And, while there have been significant advances in R&D productivity, considerable difficulties remain regarding the use of data and analytics which are needed to solve the more formidable industry-facing challenges.

For companies that overcome the challenges, tremendous opportunity exists to drive sustainable growth over the next decade through technology and analytics-driven business innovation. By first addressing the challenges, pharmaceutical companies can follow a proven path to take control of internal and external data relevant to products, markets, and customers, and gain the improved insight they need to create sustained growth, and provide innovative and cost-effective solutions for patients.

Value-based payment and its impacts

In the new health economy, the impacts of the shift to value-based payment are far-reaching. Payers are demanding outcomes-based pricing, and one in four health plans already have a value-based plan for pharmaceuticals. Health care policymakers and payers are increasingly mandating what and how providers can prescribe medication. With more focus on prevention, there is increasing scrutiny over treatment, especially as it relates to cost and value. One of the most important trends is the transformation of the patient’s role from passive recipient to engaged, savvy consumer. As patients take responsibility for higher out-of-pocket costs, they are more actively involved in all aspects of health care decision making and take into consideration cost-benefit, value and expected outcomes.

Data-driven insight needed

As companies work to adapt to these changes, the pressure for data-driven decision-making increases. The volume of data from a growing variety of sources is vast and easily attainable. Pharmaceutical companies no longer have a monopoly on information about their products, as many companies have emerged to perform analytics for all stakeholders. In parallel, social media have accelerated information sharing in unexpected ways. For example, social media sites give patients a forum to discuss therapies, costs, side effects and efficacy, making the analysis – well-formulated or not – available to inform policy decisions that affect access, pricing and product appropriateness.

Accordingly, pharmaceutical companies are increasing investment in new data sources, the use of real-world evidence, new joint ventures with payers and providers, and partnerships with Silicon Valley players. Yet, internal barriers to effective data-driven decision-making persist. For most companies, data exists in silos and there is little if any collaboration across groups. In fact, companies admit they purchase the same data multiple times for multiple projects. The typical ad hoc, one-off approach may provide specific answers, but lacks the ability to scale to address ongoing needs. Add to that a lack of interoperability among systems and a lack of data integration, all of which makes return on investment minimal.

A better way: A strategic technology platform to support iterative analytics

In today’s complex environment, effective use of data depends upon the ability to integrate rapidly, experiment effectively with results, and scale up successful outcomes quickly. This process is best supported by a technology platform that enables nimble integration of large, disparate data sets into a common data model, analytics to support the entire development life cycle, and the ability to scale flexibly to meet business demands. By centralizing data management, storage, access, and analytics, new benefits emerge:

• Data sets can be used and reused across many projects for different use cases while fees and subscriptions are managed centrally and consistently.

• Standardization and optimization of the framework and processes for data ingestion, processing and quality assurance.

• Tools for advanced analysis, such as machine learning and Natural Language Processing (NLP), can be put in place for use by multiple groups.

• Tools for communication and collaboration can make each project’s results, publications, guidance and insights available to other groups to be applied in new ways.

The shift to iterative analytics: A maturity model

For most companies, a tactical approach appears to cost less initially; however, the benefits of a platform approach multiply over time. A platform can be built out and expanded incrementally to mature toward a solution that enables simulation and modeling coupled with predictive, prescriptive and cognitive analytics. For example, starting with a limited number of use cases, the organization can build out the foundation for data ingestion, normalization and de-identification, and then add processing and quality assurance along with the presentation, visualization and analytics capabilities. Over time, new data and new tools can be incorporated into the existing framework to address new use cases – without duplicating ramp-up time and costs.

As data sources and formats continue to expand, companies can explore new use cases, such as translational research, site feasibility, comparative effectiveness, reimbursement analysis, or adverse event incidence rates. Benefits become especially clear when organizations consider data from multiple sources in multiple countries where some of the countries restrict use across geographical boundaries. With a platform approach, data ingestion and management can support the level of detailed auditing needed. The return on the initial investment grows exponentially as use cases are added and as results are shared and reapplied across the organization. What’s more, as the platform scales, advanced capabilities, such as machine learning and natural language processing can be incorporated. While companies could not justify these advanced capabilities within solitary projects, a platform approach makes this level of scale possible.

Key takeaways for delivering value from data analytics

To build a successful platform-based data analytics strategy, there are several things to keep in mind.

• Strong leadership is critical to drive strategic initiatives across the organization. Leadership is needed to support investment and encourage experimentation. Fundamentally, data must be viewed not from disjointed, siloed viewpoints, but as a valuable, organizationally-owned asset.

• Identify the highest priority use cases, building gradually and then scaling up according to a road map toward the final vision.

• Keep the functional s in mind. For example, researchers explore and use data differently than clinical trial teams. In the same fashion, data sources and formats should remain agnostic.

By facing challenges head on and managing the myriad of internal and external data relevant to products, markets, and customers, companies can gain the improved insight they need to succeed in a rapidly changing environment.

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